Financial Stability and Innovation: The Role of Non-Performing Loans

Abstract

This study analyses the relationship between non-performing loans (NPLs) and innovation systems at a global level. The data were obtained from the World Bank and the Global Innovation Index over the period 2013–2022 for 149 countries. The k-means algorithm was used to verify the presence of clusters in the data. Since k-means is an unsupervised machine-learning algorithm, we compared the Silhouette coefficient with the Elbow method to find an optimization. The results show that the optimal number of clusters is three, as suggested using the Elbow Method. Furthermore, a panel data analysis was conducted. Results show that the level of NPLs is positively associated with cultural and creative services exports as a percentage of total trade and innovation input sub-index and negatively associated with the Hirsch Index, ICT services exports as a percentage of total trade, ICT services imports as a percentage of total trade, and information and communication technologies.

1. Introduction

Financial stability and innovation are two pillars essential for the economic health and development of nations. By definition, NPLs are a class of loans currently in default or considered likely to default and reflect turmoil or distress in the banking sector. High levels of NPLs often reflect more general macroeconomic problems, such as limited credit supply, slower growth in the economy, and lower investor confidence. Because innovation is a key driver of long-term growth, determining how NPLs affect innovation is central to formulating effective policies. Innovation systems, such as those in the ICT and Cultural and Creative Services (CCS) industries, rely heavily on access to financial means to take action in areas like research, development, and expansion. High NPL levels usually lead financial institutions to follow stricter lending standards, which in turn decreases the availability of lending, particularly to riskier sectors. Showing contractions in this credit can dampen innovation because companies that need financing for technological advances or creative projects will not be able to acquire the capital. On the other hand, industries such as CCS are also robust during economic unrest because of the fact that they have an inherent resilience and capacity to access forms of finance elsewhere. The analysis of this relationship, therefore, conveys to the policymakers adequate insight into conducting focused interventions: supposing the high magnitudes of NPLs fall on few sectors, the government can provide policies supporting such industries with subsidies or with the help of alternative financing facilities. Understanding these connections helps develop regulatory frameworks that balance financial stability and the need to foster innovation. The significance of this analysis underlines the economic policy for financial health and innovation, both being equally important for continued economic growth and competitiveness around the world.

NPLs, loans that are in default or close to defaulting, are a crucial metric for assessing the health of financial institutions and, by extension, the broader economy. High levels of NPLs indicate potential vulnerabilities within the financial system, reflecting borrowers’ inability to meet their debt obligations, which can lead to cascading economic consequences. The significance of NPLs extends beyond their immediate impact on banks’ balance sheets. They are also indicative of broader economic conditions and can influence the trajectory of financial innovation. When financial institutions are burdened with high levels of NPLs, their capacity to lend diminishes, leading to a contraction in available credit for businesses and consumers. This contraction can stifle economic growth and inhibit the adoption of innovative financial products and services. Conversely, a stable financial environment with low NPL levels fosters a conducive atmosphere for innovation, allowing financial institutions to take on calculated risks and invest in new technologies and methodologies that drive economic progress [1].

The relationship between NPLs and financial innovation is multifaceted. On the one hand, financial innovation can lead to an initial increase in NPLs due to the introduction of new, untested products that may pose higher risks. For instance, the subprime mortgage crisis in the United States highlighted how innovative financial products, such as mortgage-backed securities, could lead to significant financial instability when not managed properly. On the other hand, innovation in financial technologies and risk management practices can help mitigate the risks associated with NPLs. Advanced data analytics, Machine Learning (ML), and blockchain technology are examples of innovations that can enhance the accuracy of credit risk assessments, improve loan monitoring, and streamline the resolution of NPLs, thereby contributing to financial stability. The economic implications of NPLs are profound, affecting not only financial institutions but also the broader economy. High levels of NPLs can erode banks’ capital bases, reduce profitability, and constrain their ability to provide new loans. This, in turn, can lead to a credit crunch, where businesses, especially small and medium-sized enterprises (SMEs), find it difficult to obtain the financing needed for growth and innovation. In economies heavily reliant on banking sector credit, this can slow down overall economic activity and impede the development of new industries and technologies [2,3].

Moreover, the management of NPLs is crucial for maintaining financial stability. Effective NPL management strategies include asset quality reviews, the establishment of asset management companies (AMCs), and the development of legal frameworks for insolvency and debt recovery. These measures help clean up banks’ balance sheets and restore confidence in the financial system. Countries that have implemented robust NPL management frameworks, such as Italy with its GACS (Garanzia Cartolarizzazione Sofferenze) scheme, have seen improvements in financial stability and a gradual reduction in NPL levels. Financial stability and innovation are particularly important in the context of achieving sustainable development goals (SDGs). Sustainable development requires a stable financial system that can support long-term investments in infrastructure, renewable energy, and social programs. High NPL levels can divert resources away from these critical areas, undermining efforts to achieve sustainable development. Conversely, financial innovation can play a significant role in advancing sustainable development by creating new financing mechanisms, such as green bonds and social impact bonds, that channel funds into projects with positive environmental and social outcomes [4,5].

The role of regulatory frameworks in balancing financial stability and innovation cannot be overstated. Regulatory bodies must ensure that financial institutions maintain adequate capital buffers and adhere to prudent lending practices to prevent the buildup of NPLs. At the same time, regulations should not stifle innovation. A balanced approach is needed, one that encourages the adoption of new technologies and financial products while safeguarding against systemic risks. For example, the Basel III framework, which emphasizes capital adequacy, stress testing, and market liquidity risk, provides a comprehensive approach to managing risks while allowing room for financial innovation. In addition, the global financial landscape is interconnected, meaning that financial instability in one region can have ripple effects worldwide. The 2008 financial crisis demonstrated how interconnected global markets are and how financial innovation, in the form of complex derivatives, could amplify systemic risks. Therefore, international cooperation and coordination in financial regulation are essential for managing the risks associated with NPLs and promoting global financial stability. Institutions such as the International Monetary Fund (IMF) and the Financial Stability Board (FSB) play crucial roles in fostering international dialogue and setting global standards for financial regulation [6,7].

The advent of fintech and digital banking has further complicated the relationship between financial stability and innovation. Fintech companies, with their innovative approach to financial services, have the potential to significantly reduce NPLs through better credit scoring models and more efficient loan processing systems. However, the rapid growth of fintech also poses regulatory challenges, as traditional regulatory frameworks may not be equipped to address the unique risks associated with digital financial services. Regulators must adapt to these changes and develop new strategies to monitor and manage risks in the fintech sector. Moreover, the role of NPLs in shaping investor sentiment and market confidence is significant. High levels of NPLs can lead to a loss of confidence among investors, resulting in higher funding costs for banks and a potential withdrawal of capital from the financial system. This loss of confidence can exacerbate financial instability and create a vicious cycle where deteriorating financial conditions lead to further increases in NPLs. Therefore, maintaining a low level of NPLs is crucial for sustaining investor confidence and ensuring a stable financial environment conducive to innovation and growth [8,9].

NPLs are crucial in the relationship between financial stability and innovation. While high levels of NPLs can signal financial distress and hinder economic growth, effective management and regulatory strategies can mitigate these risks and create a stable environment that fosters innovation. The interplay between NPLs, financial stability, and innovation underscores the need for a balanced approach that promotes economic development while safeguarding against systemic risks. By addressing the challenges associated with NPLs and leveraging the potential of financial innovation, policymakers can ensure a resilient and dynamic financial system that supports sustainable economic growth.

This paper addresses the link between financial stability (proxied through NPLs) and innovation systems. Only a few studies have focused on the impact of financial instability on macroeconomic variables like GDP growth, unemployment, and inflation. At the same time, the link between those NPLs and sector-specific innovation remains largely unexplored. However, this gap becomes more critical in research involving industries such as CCS and information and communication technology (ICT), which, unexpectedly, respond very differently to financial instability. In this respect, the literature analyses how financial crises and banking instability affect overall economic performance or technological progressions at an aggregate level. This study goes one step further by analyzing the effect on distinct innovation sectors, represented by CCS and ICT, underlined by the potential for financial stress to differentially support or hinder innovative activities. From a methodological perspective, this research proposes a new approach to analyzing NPLs that fine-tunes the clustering process, therefore enabling deeper analysis regarding the behavior of various economic structures with NPL dynamics.

This paper uses data from 149 countries on financial and innovation indices like cultural and creative services exports, ICT exports and imports, and the H-Index. In this respect, it highlights a complex approach toward how NPLs—usually regarded as indicators of financial distress—correlate with a country’s capability to innovate in particular sectors. Moreover, by using the k-means clustering algorithm optimized with the Silhouette coefficient and the Elbow method, this article analyses global data to detect well-focused country clusters according to the level of their NPLs. This clustering methodology enables countries to be classified according to profiles of similar finances and innovations, offering more evidence of the nexus between financial stability and innovation. The results also show a positive relationship between NPLs and CCS exports. This contradicts the perceived understanding that financial instability is universally harmful to innovation. Instead, this article indicates that financial difficulties in certain industries, such as those in creative sectors, could coincide with strong export performance, particularly in developing economies. On the other hand, the negative association of NPLs with ICT services means that financial instability directly impedes technological improvement and is another dimension of risk the ICT sector faces in unstable financial environments. This duality of innovative sectors offers a more subtle understanding of how creative and financial industries are susceptible to financial crises differently. The findings give useful indications for policymakers to balance financial stability with innovation.

One key implication is the need for targeted, sector-specific policy interventions. This study reveals that rising NPLs do not affect all innovation sectors equally. While high NPLs are generally considered a sign of financial distress, this study finds a positive relationship between NPLs and CCS exports. This suggests that CCS sectors demonstrate resilience or even thrive in times of financial instability. Policymakers must focus on providing alternative sources of financing, such as government-backed loans or tax incentives, to ensure the continuous development of ICT sectors even during times of financial crisis. Supporting technological advancement in this manner is essential to maintaining a country’s global competitiveness, particularly as ICT plays a critical role in the digital economy. Another important implication is the effect of financial instability on academic productivity, as measured by the H-Index. This study finds a negative correlation between NPLs and research output, indicating that financial distress limits funding for research and development (R&D). This reduces the production of high-quality, citable research, ultimately lowering a nation’s intellectual and innovation capacity. For governments, this underscores the importance of maintaining or even increasing funding for academic institutions during financial crises to avoid long-term intellectual stagnation. Moreover, this study highlights an unexpected relationship between NPLs and the Innovation Input Sub-Index (ISI). Financial crises often force banks and businesses to innovate as a means of survival, especially in risk management and regulatory compliance. Policymakers could leverage this by encouraging innovation in the financial sector through the adoption of new technologies like ML and blockchain to improve risk management and reduce future NPLs. Such innovations not only help financial institutions manage crises more effectively but also contribute to the broader financial stability of the economy. In conclusion, this study demonstrates that the relationship between financial stability and innovation is highly complex and sector-specific. It calls for nuanced, targeted policy measures that consider the differing impacts of NPLs across various industries. By focusing on supporting vulnerable sectors like ICT and capitalizing on the resilience of creative industries, policymakers can foster innovation and economic growth even in the face of financial instability. Additionally, encouraging financial sector innovation during crises can further stabilize economies and promote long-term development.

This paper tries to fill a gap in the existing literature, i.e., the relationship between banking instability and innovation. While previous studies have kept a greater focus on how financial instability—measured through NPLs—affects macroeconomic indicators of GDP growth, unemployment, and inflation, few studies have focused on the specific relationship between NPLs and sectoral innovation. This study tries to explore exactly that niche by assessing the impact of NPLs on a set of variables that capture the degree of innovation at the country level. Thus, this article creates a connection between a classical variable used to evaluate banking stability, NPLs, and economic innovation, which is a rising area of interest in economics due to the effects of the Fourth Industrial Revolution on firms’ productivity and GDP growth.

Non-performing loans (NPLs) are typically indicators of financial distress but their effects vary across sectors. In industries like information and communication technology (ICT), which rely heavily on credit for research and development, high levels of NPLs reduce credit availability, stifling innovation; however, creative sectors, such as Cultural and Creative Services (CCS), are more resilient to financial instability, with a positive correlation between NPLs and exports. This highlights that financial instability affects sectors differently, prompting the need for targeted policy interventions. While sensitive industries like ICT may require alternative financing solutions during economic turbulence, creative industries benefit from supportive policies like tax incentives or government-backed loans. This article also underscores the role of financial innovation, such as machine learning and blockchain, in mitigating NPL risks by improving risk assessment and loan monitoring. Policymakers are encouraged to adopt frameworks like Basel III to ensure financial stability without stifling innovation. Financial stability is crucial for global competitiveness, as countries with low NPL ratios are better positioned to foster innovation. At the same time, those with high NPL levels struggle to invest in new technologies, widening the gap between advanced and developing economies.

The rest of the paper proceeds as follows. Section 2 illustrates the relevant literature. Section 3 describes the methodology and the data. Section 4 presents a performance comparison between various types of machine-learning algorithms for clustering. Section 5 shows the results of clustering with the k-means algorithm. Section 6 contains the panel data estimates. Section 7 offers economic policy implications. Section 8 discusses the results, and Section 9 concludes the results.

2. The Literature Review

2.1. Financial Innovation and Stability

This section examines the relationship between financial innovation and stability, revealing both its potential benefits and risks. Ref. [10] emphasizes the need for the European Union (EU) to balance innovation with financial stability, as regulatory frameworks and market demands shape financial dynamics, especially during economic uncertainty. Ref. [11] highlights the EU’s distributed ledger technology pilot regime as an example of balancing innovation with investor protection and stability. Similarly, Ref. [12] explores how financial innovation can boost economic growth by deepening financial markets while cautioning that inadequate regulatory oversight could threaten stability. Ref. [13] finds that financial innovation in the ICT sector can lower bankruptcy risks by offering more flexible financing options. Ref. [14] shows that supportive institutional environments encourage banks to adopt innovative financial products, leading to growth and stability. Ref. [15] argues that technological finance boosts economic resilience by improving efficiency and reducing systemic risks. Ref. [16] stresses the need for financial innovations to align with sustainable development goals to achieve long-term stability. Ref. [17] highlights that firms integrating innovation are better equipped to adapt to changing market conditions. Ref. [18] demonstrates through the Korean food industry that open innovation, including partnerships and knowledge sharing, enhances financial sustainability. Ref. [19] finds that competitive pressures in European banks drive innovation, improving efficiency and risk management. Ref. [20] argues that central bank independence is key to regulating financial innovation effectively. Ref. [21] emphasizes the importance of innovative financial strategies in helping crisis enterprises navigate economic downturns and maintain solvency. Ref. [22] finds that energy firms in transition economies with concentrated ownership and innovative practices are more likely to achieve financial sustainability. Ref. [23] demonstrates that strong corporate governance enhances the stability benefits of financial innovation. Ref. [24] highlights that financial innovation, while driving economic sustainability, also introduces risks that must be managed, especially in times of policy uncertainty. Ref. [25] concludes that innovative financial products improve banks’ risk management and performance. Ref. [26] shows that diversified banks in OECD countries are better equipped to weather financial crises and maintain stability. Ref. [27] emphasizes the importance of balancing profitability and stability in financial innovation, especially during periods of shareholder value management. Ref. [28] finds that banks leveraging intellectual capital efficiently are more likely to achieve financial stability. Ref. [29] provides cross-country evidence showing that financial inclusion and competitive markets contribute to stability. Ref. [30] highlights how banks’ innovation capacity during the Global Financial Crisis enhanced their competitiveness and stability. Ref. [31] argues that inclusive and innovative financial systems are key to maintaining macroeconomic stability. Ref. [32] emphasizes the importance of stable regulatory environments for supporting innovation networking. Ref. [33] suggests that while financial innovation improves capital allocation efficiency, it also introduces risks that must be managed. Ref. [34] emphasizes the importance of adaptive central banking policies to ensure financial stability in response to evolving financial landscapes. Ref. [35] uses agent-based modeling to show how financial innovation, such as mortgage securitization, can influence economic growth and stability. Ref. [36] highlights the risks fintech poses to financial stability, despite its benefits, by introducing new vulnerabilities. Ref. [37] calls for a holistic approach to financial regulation, focusing on the macroeconomic roots of financial stability to manage risks from innovation. Ref. [38] finds that fintech enhances global financial stability through improved financial inclusion and efficiency.

2.2. Technological Innovation and Sustainable Development

This sub-section delves into how technological innovations contribute to sustainable development and financial stability. Financial innovation has been recognized as a critical driver of economic growth and stability, particularly in the context of achieving SDGs in emerging economies. Ref. [39] highlights the interconnected role of financial stability, technological innovation, and renewable energy in fostering sustainable development in BRICS countries, with financial stability being key to implementing innovations that drive economic growth. Similarly, Ref. [40] shows that in Bangladesh, combining financial development, trade openness, and technological innovation reduces energy intensity, promoting sustainable growth. Both studies emphasize the importance of integrating financial and technological strategies to achieve long-term sustainability. Ref. [41] argues that effective innovation management is crucial for ensuring the security and stability of financial systems amid rapid digitalization. Ref. [42] adds that technological innovation will reshape financial regulation, requiring a re-evaluation of regulatory frameworks. Ref. [18] highlights the positive impact of open innovation on financial sustainability, showing that collaborative R&D and knowledge sharing enhance adaptability in industries facing technological advancements. Ref. [43] emphasizes the role of financial innovation in driving economic growth by increasing market efficiency. Ref. [44] demonstrates that fintech-based open innovation can improve financial inclusion and development, contributing to economic stability in emerging economies. Ref. [45] finds that external factors such as market competition and regulatory changes significantly influence innovation strategies in financial services, impacting competitive advantage and stability. Ref. [46] suggests that banks should tailor their innovation strategies based on their financial health and life cycle stage to ensure sustainable growth. Ref. [47] warns that while financial innovation brings economic benefits, it also introduces risks, as seen in the US credit crisis, necessitating careful management. Ref. [48] highlights the complexity of managing innovation dynamics, stressing the need for a comprehensive approach to sustain economic stability. Ref. [49] emphasizes that political stability fosters innovation and development in Africa. Ref. [50] calls for adaptive regulatory frameworks to address the evolving financial landscape. Ref. [51] proposes a matrix model for managing global financial innovation, advocating for structured innovation management to enhance financial system stability and development. Ref. [52] uses an evolutionary game theory model to demonstrate that adaptive regulatory strategies are crucial for maintaining a balanced and stable financial system. Ref. [53] highlights the need for robust regulatory frameworks to manage the risks of digitalization, emphasizing the integration of innovation and security strategies for long-term financial stability. Ref. [54] explores the complex relationship between credit markets, economic output, and productivity, noting that well-developed financial systems promote growth through investment and innovation but caution against the risks of excessive credit. Ref. [55] examines how financial development boosts economic growth by improving capital allocation and fostering entrepreneurship, though they warn of risks like financial bubbles. Ref. [56] focuses on agricultural growth, showing that financial development enhances agricultural productivity by improving credit access for farmers and facilitating investments in advanced technologies. Their findings underscore the importance of financial systems in sector-specific growth and the potential of advanced analytical methods in economic research. Ref. [57] explores the interplay between innovation, logistics performance, and environmental sustainability, finding that while innovation and efficient logistics enhance economic performance, they can lead to environmental degradation. The study emphasizes the need to balance these factors through policies that encourage innovation and logistics improvements alongside strict environmental regulations. Ref. [58] examines the drivers of green innovation and renewable energy adoption, identifying key factors like government policies, technological advances, and market incentives that accelerate the transition to sustainable energy. They highlight the importance of addressing barriers with a coordinated approach. Ref. [59] focuses on how digitalization, through technologies like IoT and AI, can improve resource management and promote sustainable natural resource use. They provide case studies demonstrating how technological innovation can reduce environmental impact and support sustainable development goals.

2.3. Market Dynamics and Competitive Advantage

This sub-section examines how innovation drives market dynamics and competitive advantages across various sectors. The relationship between financial innovation and economic growth is complex, encompassing a range of factors, including financial markets, venture capital, and technological advancements. Ref. [60] analyzes the interconnected dynamics of innovation, financial markets, venture capital, and economic growth in Europe, demonstrating how these factors create a virtuous cycle that fuels economic development. Ref. [61] provides a theoretical framework for the financial innovation process, emphasizing the critical role of regulatory environments and market demands in fostering innovation. Ref. [62] explores how open innovation practices in entrepreneurial contexts enhance firms’ innovation capacity, contributing to economic growth through collaboration and knowledge sharing. Ref. [63] applies a Schumpeterian lens to financial innovations, highlighting the tension between creative destruction and economic stability, particularly in the digital age. Ref. [64] emphasizes the importance of strong institutional frameworks and supportive policies in fostering corporate innovation, which is key to sustainable development. Ref. [65] discusses the regulatory challenges of financial innovation, stressing the need for adaptive frameworks that balance the benefits of innovation with the necessity of maintaining financial stability amid rapid technological change. Ref. [66] finds that economic policy uncertainty significantly affects financial innovation, emphasizing the need for stable and predictable policy environments to foster innovation and maintain long-term stability. Ref. [67] shows that participation in open innovation communities enhances firms’ innovation capabilities, improving their performance and stability. Ref. [68] highlights the risks of liquidity illusion associated with complex financial instruments, noting that financial innovations can create false perceptions of stability, potentially leading to crises. Ref. [69] reinforces the need for transparency in financial innovation, arguing that it is crucial for maintaining investor confidence and ensuring financial system stability, especially in post-crisis settings.

Overall, the literature highlights the intricate and multifaceted relationship between financial innovation, economic growth, and financial stability. Financial innovations have the potential to drive economic development and enhance financial stability by improving efficiency, inclusivity, and adaptability; however, these benefits come with inherent risks that must be carefully managed through effective regulatory frameworks and innovation management practices. The studies reviewed underscore the importance of aligning innovation strategies with institutional policies, ensuring transparency in financial practices, and adopting adaptive regulatory approaches to mitigate the risks associated with financial innovations. By balancing these elements, it is possible to harness the benefits of financial innovation while maintaining the stability and sustainability of financial systems.

A summary of the literature reviewed is given in

Table 1. Summary of the relevant literature.

Macro Theme	References
Financial Innovation and Stability	[10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38]
Technological Innovation and Sustainable Development	[18,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59]
Market Dynamics and Competitive Advantage	[60,61,62,63,64,65,66,67,68,69]

below.

2.4. A Critical Analysis of the Main Concepts in the Literature Review

The literature on the relationship between innovation and financial stability underlines both the benefits of this interaction and its intrinsic risks. While financial innovation has been hailed as a driver of economic growth and resilience, in particular through gains in efficiency, enhanced risk management, and increased competitiveness, the literature equally emphasizes that it may be a source of financial instability if not appropriately regulated. Another key theme is the role of regulatory frameworks in dealing with the dual-edged nature of financial innovation. Some of the analyzed studies reinforce the need for a comprehensive balance, particularly within the EU, where innovations like the DLT pilot regime themselves are designed to work on growth while ensuring investor protection and financial stability. Poor regulations might foster systemic fragilities. The literature identifies one overwhelming theme: while financial innovation provides a better capacity for withstanding economic shocks, it also introduces new risks. Technology-based finance or innovative financial products, such as those developed during the Global Financial Crisis, can bring about advantages in risk management. However, a part of this innovation, like mortgage securitization and other complex financial innovations, conventionally creates a liquidity illusion, leading people into a false sense of security, which may culminate in a financial crisis. This again intimates the need for adaptive regulatory strategies evolving with financial innovations. Central bank independence is often mentioned as giving the added flexibility to regulate and balance that innovation with the need for stability. Another major theme concerns the macroeconomic implications of financial innovation and how this impacts diversity in both institutions and instruments. Research indicates that the major beneficiaries of financial innovation are economies with a low NPL ratio and a high level of financial inclusion, and such innovation has, as a consequence, the effects of causing the economy to grow and making markets more efficient. On the other hand, countries with high levels of NPLs or weak regulatory frameworks can hardly benefit from financial innovation; instability dampens the investment in new technologies. This underlines the fact that a stable financial environment and good governance are essential to fully realizing the potential of financial innovation. In sectors like ICT, for instance, competition transitively calls for innovation, which in turn works to improve risk management and operations. Where other industries are concerned, such as energy companies in transition economies, the combination of concentrated ownership and innovative practices marches hand in hand with financial sustainability. This further justifies the diversified impact of financial innovation, particularly on industries, and, therefore, calls for frameworks of regulation that address the range of needs that differ among individual sectors. New financial instruments could facilitate increased economic stability and growth but a substantial risk is also associated with such financial innovation. As indicated in the literature, an adaptive regulatory framework requires open lines of governance and central bank independence to ensure that regulation strikes a balance between the potential benefits from innovations and the need to prevent financial instability. This is the ultimate trade-off needed to ensure that financial innovations are supportive of long-term sustainable growth and do not create economic crises.

3. Methods and Data

A representative outline of the methodology used, along with an indication of the various analysis techniques employed, is given in Figure 1.

Advantages and limitations of using k-means clustering. Using k-means clustering in the analysis of NPLs is appropriate for several reasons, particularly when it comes to understanding the relationship between financial instability and innovation. The primary advantage of k-means clustering lies in its simplicity and computational efficiency, which makes it suitable for large datasets like the one used in the analysis of 149 countries over the period 2013–2022. By grouping countries based on their NPL ratios, k-means clustering can uncover patterns and insights that may not be immediately visible through traditional statistical methods. One key reason k-means clustering is useful in this context is that it allows for the segmentation of countries into different clusters based on their financial health, as indicated by the proportion of NPLs to total gross loans. This segmentation is important because countries with similar levels of NPLs are likely to exhibit similar financial and innovation behaviors. For example, countries with high levels of NPLs may face financial distress that limits credit availability, leading to reduced investment in innovation, particularly in capital-intensive sectors such as information and communication technology (ICT). Conversely, countries with low NPL levels might have more stable banking sectors, enabling them to better support innovation through easier access to financing. Additionally, the k-means algorithm helps identify clusters of countries that are not obvious based on geographic or economic categorizations. By relying purely on the data, k-means can group countries with similar financial characteristics, even if they are geographically dispersed or belong to different economic tiers. This is particularly valuable in a global analysis, where economic behaviors related to NPLs and innovation vary widely between regions and sectors. The clustering results can, therefore, offer a more nuanced understanding of how financial instability influences innovation across different contexts, which can inform more targeted policy interventions. Another advantage of k-means clustering is that it allows for analysis beyond simple correlations. While traditional methods might focus on the direct relationship between NPL levels and innovation outputs (e.g., ICT exports or Cultural and Creative Services exports), clustering enables a more holistic view by considering the entire distribution of NPLs across countries. This can reveal whether certain clusters of countries behave differently in terms of innovation despite having similar NPL ratios, offering insights into the role of other variables such as governance, economic diversification, or financial sector regulation. This article uses both the Elbow method and the Silhouette coefficient to optimize the k-means clustering process, ensuring that the chosen number of clusters is the most appropriate for the data. This optimization is crucial because k-means requires the user to specify the number of clusters in advance, and an incorrect choice could lead to misleading results. By carefully selecting the optimal number of clusters, the analysis ensures that the segmentation of countries is meaningful and captures the most relevant patterns in the data. However, it is also important to acknowledge some limitations of k-means clustering in this context. One challenge is that k-means assumes that clusters are spherical and equally sized, which may not always reflect the true distribution of NPLs across countries. Financial instability and innovation are complex phenomena, and their relationships may not conform to the k-means algorithm’s assumptions. Despite this, the use of k-means remains appropriate in this analysis due to its computational efficiency and ability to provide valuable insights into the global dynamics of NPLs and innovation.

Despite the advantages of using k-means clustering in the analysis of NPLs, there are a number of serious disadvantages. A major disadvantage is that k-means assumes the clusters are spherical and of similar sizes, which could fraudulently indicate the actual nature of the dispersion of NPLs across countries. Financial instability and innovation are sophisticated, multi-dimensional phenomena that may not necessarily adhere to such simple geometric structures. This can result in cluster distortions, leading to a poor mapping of the fundamental mutual relations between NPLs and innovation. Also, k-means is sensitive to the initial selection of cluster centroids: poor initialization can deflect the process of clustering to a suboptimal solution where the algorithm converges to a local minimum and fails to find the most accurate cluster structure. This could result in inconsistent, and in most cases misleading, groupings of countries for a large and heterogeneous dataset such as the one used in this analysis. Another limitation of k-means is that it requires the user to state the number of clusters in advance. Lastly, k-means struggles with outliers. Outlier countries having extreme ratios of NPL may distort the centroids of clusters. This could then affect the whole clustering process in terms of the precision of separation between the clusters.

Below, the variables used for the regression metric analysis and clustering are presented. The sources used for the construction of the database used for the analysis are also identified. The data refer to 149 countries over the period 2013–2022. The description of the variables is given in

Table 2. Description of the variables.

Variable	Definition	Acronym	Source
Bank non-performing loans to total gross loans (%)	A financial indicator that measures the proportion of NPLs relative to the total gross loans issued by a bank. This ratio is a key metric for assessing the health and quality of a bank’s loan portfolio and its exposure to credit risk. A loan is classified as non-performing when the borrower fails to make scheduled payments of interest or principal for a significant period, typically 90 days or more. Additionally, a loan may be considered non-performing if it is deemed unlikely that the debt will be repaid in full without the bank having to seize the collateral. Total gross loans, on the other hand, refer to the aggregate value of all loans granted by the bank, without deducting any allowances for potential losses or write-downs. This includes all categories of loans issued, from mortgages and personal loans to commercial and industrial loans. The non-performing loans to total gross loans (%) ratio is calculated by dividing the value of non-performing loans by the total gross loans and multiplying the result by 100 to express it as a percentage. It reflects the bank’s vulnerability to credit risk as a large number of non-performing loans can erode profitability, deplete capital reserves, and ultimately affect the bank’s ability to operate effectively [70,71].	NPL	World Bank
Citable documents H-Index	A bibliometric indicator that evaluates both the productivity and citation impact of the published academic work of a researcher, institution, or journal. The H-Index represents the highest number of papers (h) that have been cited at least h times each. This metric specifically focuses on the subset of documents that are most likely to be cited, excluding non-scholarly items such as editorials, notes, or letters to the editor. This index provides a balanced measure that combines both quantity (number of citable documents) and quality (citations per document), offering a more nuanced view of academic impact than simple citation counts or publication numbers alone. It is particularly useful for comparing the impact of researchers, journals, or institutions across different fields as it accounts for variations in citation practices among disciplines [72,73]	H-Index	Global Innovation Index
Cultural and creative services exports, % total trade	The portion of a country’s total trade that is derived from the exportation of goods and services related to cultural and creative industries. The measurement of CCS exports as a percentage of total trade involves calculating the value of these exports relative to the total value of all exports (both goods and services) from a country. This percentage provides insight into the economic significance and contribution of the cultural and creative sectors to a country’s overall trade activity. The export of CCS also helps to enhance a country’s cultural influence and soft power on the global stage [74,75,76].	CCSE	Global Innovation Index
ICT services exports, % total trade	The proportion of a country’s total exports that come from the ICT sector. This metric highlights the significance of ICT services within the broader context of a nation’s trade activities. ICT services encompass a range of activities, including software development, telecommunications, data processing, IT consulting, and other computer-related services. To calculate this percentage, the value of ICT services exports is divided by the total value of all exports (both goods and services) from a country and then multiplied by 100 [77,78,79].	ICTEXP	Global Innovation Index
ICT services imports, % total trade	The proportion of a country’s total imports that come from the ICT sector. This metric indicates the importance and reliance on ICT services within the broader framework of a nation’s trade activities. To calculate this percentage, the value of ICT services imports is divided by the total value of all imports (both goods and services) into a country, and then multiplied by 100. This calculation provides a clear understanding of how significant ICT services are to the country’s overall import profile [80,81,82].	ICTIMP	Global Innovation Index
Information and communication technologies (ICTs)	ICTs refer to a comprehensive range of technologies that facilitate the creation, storage, transmission, and management of information. These technologies include digital tools and resources such as computers, mobile phones, the internet, and cloud computing as well as traditional communication media like radio, television, and telephony [83].	ICT	Global Innovation Index
Innovation Input Sub-Index	ISI is a crucial component of the Global Innovation Index (GII), which evaluates the innovation performance of countries and economies worldwide. This sub-index assesses the elements within an economy that enable and facilitate innovative activities. It is composed of five key pillars: Institutions, which capture the political, regulatory, and business environments; Human Capital and Research, which includes education, tertiary education, and research and development (R&D); Infrastructure, which assesses information and communication technologies (ICTs), general infrastructure, and ecological sustainability; Market Sophistication, which looks at credit, investment, and trade and competition; and Business Sophistication, which evaluates knowledge workers, innovation linkages, and knowledge absorption. These pillars collectively provide a comprehensive view of the inputs necessary for fostering innovation within a country or economy. ISI, used in conjunction with the Innovation Output Sub-Index—which measures actual innovation outputs—contributes to the overall Global Innovation Index score.	ISI	Global Innovation Index

Notes: https://www.wipo.int/global_innovation_index/en/, accessed on 10 August 2024; https://data.worldbank.org/indicator/FB.AST.NPER.ZS, accessed on 10 August 2024.

.

The variables selected for this analysis are highly relevant for examining the nexus between innovation and NPLs as they capture different dimensions of a country’s innovation capacity and its interaction with financial stability. The citable document’s H-Index is an essential measure of academic and research productivity; reflecting the impact and quality of research outputs. A high H-Index indicates strong R&D performance; which is a critical driver of innovation. Since innovation often stems from scientific advancements; examining how NPLs influence academic productivity can reveal whether financial instability hampers a country’s ability to sustain its research output; which in turn could slow down innovation across various sectors. CCS exports as a percentage of total trade reflect the role of creative industries in a country’s economy. The percentage of CCS exports provides insight into how well a country is commercializing its cultural outputs on the global stage. Analyzing the relationship between NPLs and CCS exports can highlight whether financial instability affects the global competitiveness of these industries; which are often considered less capital-intensive and more resilient to traditional financial shocks compared to sectors like manufacturing. ICT services exports and ICT services imports; both as percentages of total trade; are crucial for understanding the international trade dynamics of a country’s technology sector. Exports of ICT services indicate a country’s ability to create and distribute technological innovations globally; while imports reflect its demand for foreign technology and services to support domestic innovation. The health of the ICT sector is often tightly linked to financial stability as technological development typically requires significant investment in infrastructure; research; and skills. Analyzing the relationship between NPLs and ICT trade provides a deeper understanding of how financial distress might disrupt these trade flows; potentially limiting the ability of firms to innovate due to reduced access to credit or an increased cost of borrowing. ICTs; broadly considered, encompass all digital technologies that drive innovation in modern economies. The development and adoption of ICTs are critical for enhancing productivity, fostering new business models, and improving communication and data management across industries. Financial instability could constrain the ability of firms to invest in ICT infrastructure, limiting their capacity for innovation. Thus, investigating the relationship between NPLs and ICTs is essential for understanding how financial distress may hinder a country’s broader technological progress and digital transformation. ISI measures the resources and conditions necessary to foster innovation, including R&D investment, human capital, infrastructure, and institutional support. This index captures the key inputs that enable a country to innovate and remain competitive in the global economy. Since NPLs often reflect broader financial instability, they could negatively affect the availability of innovation resources, such as reduced funding for R&D or limited access to financing for startups. By examining the relationship between NPLs and ISI, the analysis can uncover whether financial instability undermines the foundational elements required for a country’s innovation ecosystem to thrive. Together, these variables provide a comprehensive view of how different aspects of a country’s innovation system are affected by financial stability, offering critical insights into the broader relationship between NPLs and innovation.

4. A Comparison between Alternative Machine-Learning Algorithms for Clustering

As

Table 3. A comparison of clustering techniques.

Macro-Category	Clustering Algorithms	Comparative Analysis
Partition-based Clustering: Divides the dataset into a fixed number of clusters. Ideal for relatively simple data with well-defined groups.	1. k-means	k-means is generally considered better than k-medoids (PAM) and Fuzzy c-means in many situations primarily due to its computational efficiency and simplicity. k-means is faster because it calculates centroids as the average of data points within each cluster, which involves simple arithmetic operations. This allows it to scale well to larger datasets, making it more practical for real-world applications where performance and speed are essential. In contrast, k-medoids is more computationally expensive because it selects actual data points (medoids) as cluster centers and requires calculating the distance between all pairs of points, which is significantly slower for large datasets. Additionally, k-means is straightforward to implement and interpret, especially when distinct clusters are required as it assigns each point to a single cluster. While Fuzzy c-means allows points to belong to multiple clusters with varying degrees of membership, making it useful for soft clustering, this complexity can be harder to interpret and computationally expensive as it needs to update membership values iteratively. For scenarios where clear, non-overlapping clusters are needed, and the data are well-behaved (spherical clusters), k-means offers a faster, more practical, and easier-to-implement solution compared to the other two algorithms [84,85,86].
	2. k-medoids (PAM)
	3. Fuzzy c-means
Hierarchical Clustering: builds a dendrogram structure where clusters can be viewed at different levels of granularity.	1. Agglomerative Clustering	k-means is often preferred over Agglomerative Clustering, Divisive Clustering, and BIRCH due to its simplicity, computational efficiency, and scalability, especially for large datasets. Agglomerative and Divisive Clustering are hierarchical methods that either merge or split clusters iteratively. These hierarchical approaches can become computationally expensive because they require calculating distances between all pairs of points, especially as the dataset grows in size. This makes them less scalable and slower than k-means, particularly when handling large datasets with thousands of points. Furthermore, BIRCH is designed for large datasets but can sometimes perform sub-optimally when clusters are not balanced. While BIRCH is efficient for incremental clustering and handling large data streams, it requires additional preprocessing steps like threshold tuning, making it more complex to implement compared to k-means. In contrast, k-means is straightforward and easy to optimize (especially with the k-means++ initialization method), offering a more intuitive clustering process for general use cases. For tasks requiring speed, simplicity, and scalability, k-means remains a more efficient choice [87,88,89].
	2. Divisive Clustering
	3. BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)
Density-based Clustering: Identifies clusters based on dense regions of data, separating less dense regions as outliers. Suitable for noisy data and arbitrarily shaped clusters.	1. DBSCAN (Density-based Spatial Clustering of Applications with Noise)	k-means is often preferred over DBSCAN, OPTICS, and Mean-Shift for several reasons, especially when the data are well-structured, and the goal is fast, efficient clustering. k-means is simple and computationally efficient, making it a great choice for large datasets with well-defined, spherical clusters. Its time complexity is lower compared to DBSCAN and OPTICS, which can be slower due to the need to calculate distances between all points and assess local densities. While DBSCAN and OPTICS are excellent for identifying clusters of arbitrary shapes and handling noise or outliers, they require tuning parameters such as the neighborhood radius (epsilon) and minimum points (minPts), which can be difficult to optimize for every dataset. k-means, on the other hand, only requires setting the number of clusters (k) and is easy to implement. Mean-Shift is another density-based algorithm that, like DBSCAN, excels in finding clusters without pre-defining their number. However, it is computationally more expensive because it requires iterating over all data points to locate areas of maximum density. For datasets with clear, non-overlapping clusters, k-means is faster and more straightforward, while density-based methods are more suited for complex datasets with noise or irregularly shaped clusters. Thus, for speed, simplicity, and scalability, k-means often proves superior [90,91].
	2. OPTICS (Ordering Points To Identify the Clustering Structure)
	3. Mean-Shift
Model-based Clustering: utilizes probabilistic models to identify clusters, assuming that data are generated from a set of probability distributions.	1. Gaussian Mixture Models (GMM)	k-means is often considered better than GMMs, BGMMs, and HMMs in many scenarios due to its simplicity and computational efficiency. GMMs and BGMMs assume that data are generated from a mixture of Gaussian distributions and require estimating both the mean and variance for each cluster. This adds to the computational complexity as GMM uses an iterative Expectation–Maximization (EM) algorithm to find the maximum likelihood estimates, which is more computationally expensive and slower than k-means. BGMMs add another layer of complexity by incorporating a probabilistic framework that can adaptively estimate the number of clusters but it increases the computation cost further. Similarly, HMMs are designed for time series data and involve hidden states with transitions and emissions, making them unsuitable for standard clustering tasks where no temporal dependencies exist. For most straightforward clustering problems where speed and simplicity are important, k-means is superior as it quickly produces results with relatively low computational overhead, while the more sophisticated probabilistic models like GMMs, BGMMs, and HMMs are better suited for more complex data structures [92,93].
	2. Bayesian Gaussian Mixture Models (BGMMs)
	3. Hidden Markov Models (HMMs)
Graph-based Clustering: uses graph theory to identify clusters of nodes, particularly useful for network analysis.	1. Spectral Clustering	k-means is often preferred over Spectral Clustering and Community Detection algorithms like the Louvain Method or Girvan–Newman for several reasons, especially when dealing with large datasets and seeking simplicity and computational efficiency. k-means operates by partitioning the data into k clusters based on distance from centroids, making it a fast and effective algorithm for data with clear, well-defined clusters. In contrast, Spectral Clustering involves more complex steps, such as constructing a similarity matrix and performing eigenvalue decomposition on that matrix, which can be computationally expensive, particularly for large datasets. This makes k-means more scalable in terms of both speed and memory usage when compared to Spectral Clustering. Similarly, Community Detection algorithms are primarily used in network or graph-based data structures, where the goal is to find clusters of nodes based on their connections. While Louvain and Girvan–Newman are highly effective for identifying communities in graph data, they are specialized methods that are not as versatile as k-means for general-purpose clustering. Additionally, these methods often have higher computational costs due to iterative graph-based calculations. For most non-network, standard clustering tasks where simplicity, speed, and scalability are key priorities, k-means provides a more efficient and straightforward solution than Spectral Clustering or Community Detection methods [94,95,96].
	2. Community Detection (e.g., Louvain Method, Girvan–Newman)
Grid-based Clustering: divides the data space into grids and identifies clusters through the aggregation of high-density cells.	1. STING (Statistical Information Grid)	k-means is often considered better than STING and CLIQUE, particularly when simplicity, speed, and ease of implementation are key priorities. k-means time complexity is significantly lower compared to grid-based methods like STING and CLIQUE, which rely on partitioning the data space into grids. STING uses a hierarchical grid-based approach that divides the data space into cells and merges regions based on statistical attributes. While it is effective for large spatial datasets, it can struggle with arbitrary cluster shapes and requires careful tuning of the grid granularity. This can make it less flexible for general clustering tasks. CLIQUE, on the other hand, is designed for high-dimensional data and combines density-based and grid-based clustering. While CLIQUE is effective at handling high-dimensional spaces, it tends to be slower due to the need to examine grid cells in multiple dimensions and can be sensitive to parameter choices. In contrast, k-means is faster, easier to tune (requiring only the number of clusters), and works well in lower-dimensional spaces where clusters are well-separated. For most standard clustering tasks, k-means provides a more efficient and adaptable solution compared to these grid-based methods [97].
	2. CLIQUE (Clustering in QUEst)
Constraint-based Clustering: introduces constraints on the relationships between data points, allowing prior knowledge to influence cluster formation.	1. COP-KMeans (Constrained k-means)	k-means is often considered a better option than COP-KMeans and Semi-Supervised Clustering in many scenarios due to its simplicity, speed, and ease of implementation. The primary advantage of k-means over COP-KMeans is that it does not require any pre-defined constraints. COP-KMeans introduces “must-link” and “cannot-link” constraints that guide the clustering process, which, while useful in certain situations, adds complexity to both the setup and computation. This can slow down the algorithm, especially if the constraints are numerous or inconsistent with the natural structure of the data. Similarly, Semi-Supervised Clustering integrates a small amount of labeled data along with the unlabeled data to improve clustering performance. While this can lead to more accurate results in specific applications, it requires additional labeled data, which is not always readily available or easy to obtain. k-means, on the other hand, operates without any prior knowledge, making it easier to use in a wider variety of unsupervised clustering tasks. For problems where simplicity and speed are key, k-means provides a faster, more flexible, and easier-to-implement solution than constrained or semi-supervised alternatives [98,99,100].
	2. Semi-Supervised Clustering
Deep Learning-based Clustering: employs neural networks to identify latent representations and form clusters, especially effective for large and complex datasets.	1. Deep Embedded Clustering (DEC)	k-means is often preferred over advanced deep learning-based algorithms like DEC and VAE for clustering due to its simplicity, speed, and ease of implementation. DEC and VAE are significantly more complex, requiring the training of neural networks to learn latent representations of the data. This process is computationally intensive and requires substantial tuning of hyperparameters, such as the architecture of the neural network, learning rate, and the number of epochs. While DEC and VAE excel in clustering high-dimensional data and finding intricate patterns, particularly in datasets like images or text, they require a lot more computational resources and time compared to k-means. These methods also demand a deep understanding of deep learning frameworks and are less straightforward to implement. k-means, on the other hand, can be easily applied to most clustering problems with minimal configuration and still produces effective results for well-separated, low-dimensional datasets. For general clustering tasks where interpretability, speed, and efficiency are priorities, k-means is a much simpler and more accessible solution compared to these complex, resource-intensive, deep learning-based methods [101,102,103].
	2. Variational Autoencoder (VAE) for clustering

shows, the k-means algorithm is chosen based on several factors, such as the data structure and the efficiency of determining the clusters. Therefore, k-means emerges as the best ML algorithm because of its efficiency.

Therefore, despite the availability of numerous ML algorithms for performing data analysis, we specifically opted to utilize the k-means algorithm. This choice was driven by its ability to produce superior results in terms of computational efficiency and clustering performance, especially when applied to the specific data structure we are working with. The k-means algorithm not only simplifies the task of partitioning the dataset into distinct groups but also does so in a manner that reduces the computational complexity, making it a highly suitable option for our particular needs.

The k-means algorithm is a widely used clustering method due to its simplicity and efficiency but it has several important limitations. One of the primary issues is that the number of clusters must be pre-defined by the user. This means that the algorithm requires the user to specify the number of clusters in advance, which can lead to suboptimal results if the wrong number is chosen. In terms of the underlying assumptions, k-means is based on the idea that clusters are spherical, of roughly equal size, and equidistant from each other [104,105]. This assumption makes it unsuitable for datasets that contain clusters with irregular shapes, varying sizes, or different densities. In cases where clusters are elongated, irregularly shaped, or overlap, k-means struggles to accurately separate the data. Additionally, the algorithm is not robust to noise or outliers. Because k-means minimizes the squared distance between data points and their respective centroids, outliers can disproportionately influence the cluster centroids, distorting the clustering outcome. A further drawback of k-means is that it only works well with numeric data. The algorithm relies on Euclidean distance to measure similarities between points, which means it does not handle categorical or ordinal data effectively unless they are transformed into numerical representations. This limits the algorithm’s flexibility in dealing with datasets that contain non-numeric features. Moreover, k-means performs poorly when the data contains clusters with varying densities. In such cases, the algorithm may split denser clusters into several smaller ones or merge sparse clusters into a single, incorrect cluster, reducing the accuracy of the results. Another consideration is that k-means is highly sensitive to feature scaling. If the data contain features with different ranges, the algorithm tends to be biased toward features with larger magnitudes, which can skew the clustering results. It is, therefore, necessary to standardize or normalize the data before applying k-means to ensure that all features are given equal weight. Finally, although k-means is generally considered computationally efficient, it can become expensive when dealing with large datasets, especially if the number of clusters is high or the data are high-dimensional. The need to update centroids and recalculate distances between all points in each iteration can lead to high time complexity [106,107].

5. Clusterization with k-Means Algorithms

In what follows, a cluster analysis with the k-means algorithm is shown. Since the k-means algorithm is an unsupervised ML algorithm, it is necessary to find tools to help identify the optimal number of clusters. In this case, we compare the results of clusterization using the Silhouette coefficient with the Elbow method. Clustering with the k-means algorithm optimized with the Silhouette coefficient involves assigning a number to each cluster. This number ranges from −1 to 1. The closer the value is to 1, the more efficient the clustering is [108,109]. Therefore, we calculate the Silhouette coefficient value for k values between 2 and 10. The number of clusters that maximize the Silhouette coefficient occurs at k = 2 (see Figure 2).

The cluster analysis, based on the percentage of bank NPLs to total gross loans, uncovers a striking contrast between countries grouped in Cluster 1 (C1) and Cluster 2 (C2). Countries in C2 show significantly higher levels of NPLs compared to those in C1, sparking our curiosity to explore the underlying economic, political, and financial factors contributing to such differences. The complete list of countries for each cluster is given in Appendix A. C1 countries demonstrate greater economic resilience compared to C2, as evidenced by their ability to withstand external shocks and maintain financial stability. The economic structures in C1 are more diversified, reducing the reliance on a single sector and spreading the risk across various industries. In contrast, C2 countries often have undiversified economies, heavily dependent on a few sectors like agriculture or natural resources, which are more vulnerable to economic fluctuations.

However, the clustering performed with the k-means algorithm optimized with the Silhouette coefficient is not without its challenges. It results in a significant polarization of the countries, with most of them grouped in C1 and C2 being completely marginal. This imbalance in the distribution of the countries within the different clusters highlights the inefficiency of the clustering analysis. To address this, we turn to the Elbow method for the optimization of the clustering with the k-means algorithm (see Figure 3). The Elbow method provides a robust approach to ensure a more balanced distribution of countries across clusters, enhancing the reliability of our analysis.

This analysis categorizes countries into three clusters (C1, C2, and C3) based on the percentage of NPLs to total gross loans. C2 has the highest level of NPLs, followed by C1 and C3. The complete list of countries for each cluster is given in Appendix A.

C1 comprises 50 countries from various regions and different levels of economic development that exhibit a moderate level of NPLs. This cluster includes many countries, including African countries that face challenges such as political instability, economic diversification issues, and lower financial inclusion, contributing to moderate NPL levels. For Asia, countries such as Afghanistan, Bangladesh, Bhutan, and India are included. These nations are characterized by rapidly growing economies but also face structural challenges in their banking sectors, including regulatory and supervision weaknesses. Moreover, this cluster includes Eastern European countries—such as Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Hungary, and Romania—which often deal with legacy NPL issues from past financial crises and ongoing economic transitions. For the Middle East, Iraq and Lebanon are included. Political instability and economic volatility in these countries contribute to moderate NPL levels. For Latin America and the Caribbean, the presence of countries like Barbados, Curacao, Dominica, and St. Vincent and the Grenadines indicates a region with a mix of economic resilience and vulnerabilities impacting their banking sectors [110,111,112].

C2 represents countries with the highest levels of NPLs. In Europe, Greece and Cyprus stand out due to their well-documented financial crises. Both countries underwent severe banking sector distress, leading to significant accumulations of NPLs. The economic recovery in these nations has been slow, and the banking sectors continue to deal with the fallout. In the post-Soviet states, Ukraine and Tajikistan face economic challenges, including conflict, political instability, and weak institutional frameworks, contributing to high levels of NPLs. In Africa, countries like Comoros, Chad, Central African Republic, and Equatorial Guinea are characterized by fragile economies, political instability, and low financial sector development. These factors significantly impact the quality of the loan portfolios of banks. Small nations, such as St. Kitts and Nevis and San Marino, represent small economies with unique challenges. St. Kitts and Nevis, while having a relatively small banking sector, faces vulnerabilities typical of small island economies, including exposure to external economic shocks. San Marino, despite being a high-income economy, has seen its banking sector struggle with legacy NPLs [113,114,115,116].

Cluster C3 includes a broad array of different countries, totaling over 80. These countries have the lowest levels of NPLs, indicating healthier banking sectors relative to clusters C1 and C2. Many high-income and stable economies, such as Austria, Belgium, France, Germany, and The United States, are placed here. These countries typically have robust financial regulatory frameworks, diversified economies, and strong institutional capacities that contribute to lower NPL ratios. Argentina, Brazil, China, and India represent significant emerging markets with diverse economic bases and strong growth trajectories. While challenges exist, the banking sectors in these countries are relatively better positioned to manage loan portfolios. Countries such as Chile, Colombia, Costa Rica, and Malaysia are characterized by growing economies, improving financial infrastructure, and regulatory improvements that help maintain lower NPL levels. Countries like Fiji, Iceland, and Luxembourg show that smaller economies can maintain low NPL levels through sound economic policies and effective financial sector management [117,118,119].

The categorization of countries into clusters C1, C2, and C3 highlights significant differences in the health of their banking sectors. Countries in C2, with the highest levels of NPLs, face systemic risks that require comprehensive policy responses, including financial sector reforms, improved regulatory oversight, and measures to address broader economic vulnerabilities. Countries in C1, with moderate NPL levels, also need to strengthen their financial systems. These nations should focus on structural reforms, improving governance in the banking sector, and fostering economic stability to prevent an escalation of NPL levels. Countries in C3 with the lowest NPL levels provide models for effective banking sector management. The stability in these countries can be attributed to sound regulatory practices, diversified economies, and strong institutional frameworks. However, continuous vigilance is necessary to maintain this stability, especially in the face of global economic uncertainties. For C2 countries, immediate policy actions are necessary to stabilize the banking sector. This includes measures like bank recapitalization, enhancing regulatory and supervisory frameworks, and addressing macroeconomic instability. The international community can play a supportive role through financial assistance and technical support. For C1 countries, policies should focus on pre-emptive measures to avoid slipping into higher NPL categories. This involves improving credit risk management practices, strengthening regulatory oversight, and fostering economic growth that can support healthy banking sector development. For C3 countries, maintaining the status quo requires ongoing reforms and adaptations to new economic challenges. These countries should continue to innovate in financial regulation, enhance risk management frameworks, and promote economic policies that sustain growth and stability. The analysis of NPLs across these clusters provides valuable insights into the health of banking sectors worldwide. Countries in C2 face the most significant challenges and require urgent and comprehensive policy interventions. C1 countries need to strengthen their financial systems to avoid deterioration, while C3 countries, despite their relative stability, must continue to adapt and reform to maintain their low NPL levels. This clustering based on NPL ratios not only highlights the varying degrees of banking sector health but also underscores the importance of targeted policy measures to ensure financial stability and economic growth [119,120,121,122].

Therefore, the comparison between clustering with the k-means algorithm using the Silhouette coefficient and the Elbow method highlights a greater efficiency of the second method due to its ability to provide a representation of country groups that is closer to the complexity of the phenomenon at a global level.

6. Panel Data Estimates

Regarding the regression analysis, the following equation is estimated:

$$NP{L}_{it}=\alpha +{\beta }_1{\left(H-Index\right)}_{it}+{\beta }_2{\left(CCSE\right)}_{it}+{\beta }_3{\left(ICTEXP\right)}_{it}+{\beta }_4{\left(ICTIMP\right)}_{it}+{\beta }_5{\left(ICTs\right)}_{it}+{\beta }_6{\left(ISI\right)}_{it}$$

where i = 149 and t = [2013;2022]. The results are given in

Table 4. Panel data estimates.

	Fixed Effects			Random Effects
Variable	Coefficient	Std. Error	t-Ratio	Coefficient	Std. Error	t-Ratio
Costant	9.39100 **	4.28037	2.194	7.68287 ***	2.17854	3.527
H-Index	−0.238110 ***	0.0894702	−2.661	−0.0977559 ***	0.0352393	−2.774
CCSE	0.0270613 ***	0.00963331	2.809	0.0277375 ***	0.00908887	3.052
ICTEXP	−0.0294091 **	0.0130346	−2.256	−0.0206465 *	0.0121918	−1.693
ICTIMP	−0.0317156 **	0.0133400	−2.377	−0.0302631 **	0.0126215	−2.398
ICT	−0.0703288 ***	0.0167553	−4.197	−0.0791516 ***	0.0155227	−5.099
ISI	0.162938 **	0.0724317	2.250	0.125901 **	0.0495078	2.543
Statistics	SSR: 6226.843			SSR: 35189.77
	SER: 3.319783			SER: 7.268940
	Log-likelihood: −1701.586			Log-likelihood: −2283.499
	AIC: 3617.172			AIC: 4580.999
	SBIC: 4099.769			SBIC: 4612.571
	HQIC: 3804.075			HQIC: 4593.226
	LSDV F(106,565): 26.31792 (7.1 × 10−12)
	Obs: 672			Obs.: 672

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10.

.

According to the panel data regression results, the level of NPL is positively associated with CCSE, encompassing sectors like media, design, entertainment, and software, which are increasingly recognized as vital components of modern economies. As these sectors grow and contribute a larger share to total trade, they influence various economic indicators, including the NPL ratio in the banking sector. Understanding why these two variables might rise together requires analyzing their underlying economic interactions. Economic diversification through an increase in CCS exports can have a dual effect; while it provides new revenue streams and reduces dependence on traditional sectors, it also involves inherent risks due to the volatile nature of creative industries. The financial risks associated with funding these innovative but uncertain ventures can lead to higher loan defaults, increasing the NPL ratio [75]. This risk is particularly acute during economic downturns or market shifts that disproportionately affect the creative sectors. The creative industries often face significant market fluctuations due to changing consumer preferences, technological advancements, and intense competition. These fluctuations can result in unpredictable income streams for businesses in this sector, making them more prone to defaulting on loans. Banks exposed to these sectors might see an increase in NPLs as businesses struggle to maintain consistent revenue, especially during periods of economic instability [123]. The cultural and creative sectors often require substantial upfront investments in technology, talent, and infrastructure. The high capital intensity and longer gestation periods before seeing returns can strain the financial health of companies within these industries. As banks extend credit to support these investments, the likelihood of defaults increases if the expected returns are not realized in a timely manner, thereby raising the NPL ratio [74]. While cultural and creative exports can offer stable long-term earnings, their initial stages are often marked by high volatility. The uncertainty in achieving stable earnings can lead to financial distress among businesses, resulting in higher default rates on loans. This trend is exacerbated in economies that heavily invest in boosting their cultural exports without adequate financial safeguards. External economic factors, such as global market demand and exchange rate fluctuations, can significantly impact the earnings from CCS exports. Adverse changes in these factors can lead to unexpected financial shortfalls for businesses, increasing their risk of loan defaults. This external volatility is transmitted to the banking sector, resulting in a higher NPL ratio [124]. The growth of CCS exports as a percentage of total trade can lead to an increase in the NPL ratio due to the high volatility, substantial investment requirements, and market uncertainties inherent in these industries. While these exports contribute positively to economic diversification and innovation, their financial instability and associated risks can strain the banking sector, leading to higher loan defaults. Understanding this relationship underscores the need for careful financial planning and risk management to support sustainable growth in both the creative industries and the broader economy (see Figure 4).

To better understand the reasons for the positive relationship between the value of non-performing loans and the value of CCS, we summarize the arguments in the following

Table 5. Factors contributing to the positive correlation between NPLs and CCS values.

Motivation	Explanation	Sources
Economic Diversification	CCS provides alternative revenue streams when traditional sectors struggle, leading to growth in creative exports despite financial instability contributing to higher NPLs.	[125,126]
Public and Alternative Funding	Creative industries often rely on public funding or alternative finance. When they do take formal loans, their irregular revenue streams may lead to higher default rates and NPLs.	[127,128]
Resilience of Creative Industries	Creative industries are adaptable and less capital-intensive, allowing them to expand even in economic downturns while other sectors face difficulties, leading to rising NPLs.	[129,130,131]
Risk and Innovation in Creative Sectors	The creative sector is driven by risk-taking and innovation, which can lead to growth but also higher loan defaults, increasing NPLs while CCS continues to contribute positively to exports.	[132,133,134]
Demand for Cultural Exports	Global demand for cultural exports remains strong even in economic crises. This allows CCS to thrive internationally, while domestic financial conditions worsen, raising NPL levels.	[135,136,137]
Freelance and Small Business Dominance	The dominance of freelancers and small businesses in CCS makes them vulnerable to financial stress. When loans are taken out, defaults are common, contributing to NPLs despite continued export activity.	[138,139]
Government Support for Soft Power	Governments may support CCS to enhance cultural diplomacy. This investment drives exports but also creates higher risks for NPLs as government-backed loans may be less rigorously managed.	[140,141]
Financial Exclusion and Informality	Many CCS businesses operate outside traditional financial systems. When they do seek loans, their informality increases the likelihood of NPLs but their economic contribution through exports remains significant.	[125,142,143]
High Volatility in Revenue	The cultural economy is highly subject to changing trends, resulting in fluctuating income. This leads to higher chances of loan defaults and increased NPLs, while the sector’s global export performance remains robust.	[144,145]
Cultural Resilience in Times of Crisis	During financial crises, cultural products maintain a strong demand as consumers turn to entertainment and art. This sustains CCS export growth while the financial sector faces rising NPLs.	[129,146,147]

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In addition, it is found that the increase in ISI increases NPLs as well, which can be explained through several interconnected mechanisms. This relationship may initially appear counterintuitive, as high levels of NPLs generally indicate financial distress; however, upon closer examination, the dynamic interplay between financial stress and innovation reveals a more complex narrative. Firstly, NPLs often signify periods of economic downturn or financial instability, which create an environment that necessitates innovation. During such periods, banks and financial institutions are compelled to rethink and revamp their risk management strategies and lending practices. The need to mitigate future risks and recover from financial losses drives these institutions to adopt innovative approaches and technologies. For instance, the aftermath of financial crises has historically led to the development and implementation of advanced risk assessment tools, better credit scoring models, and more robust regulatory frameworks [148]. Moreover, the presence of high NPLs can trigger policy interventions and regulatory changes that foster innovation. Governments and regulatory bodies, in response to rising NPLs, may introduce measures aimed at improving financial stability and encouraging sustainable lending practices. These measures can include stricter capital requirements, enhanced oversight, and incentives for technological adoption in the banking sector. Such regulatory changes can act as catalysts for innovation, pushing banks to develop new products and services that align with the revised regulations and market needs [149]. Economic uncertainty, often reflected by high NPL ratios, also plays a crucial role in fostering a culture of innovation. During uncertain times, both businesses and financial institutions seek to diversify their portfolios and reduce dependency on traditional revenue streams. This diversification often leads to investments in R&D, the adoption of cutting-edge technologies, and the exploration of new markets. For example, during the COVID-19 pandemic, many banks increased their investment in digital banking solutions to cater to the changing needs of consumers and ensure a continuity of services [144]. Furthermore, high levels of NPLs can influence the competitive dynamics within the banking sector. Banks with lower levels of NPLs may capitalize on their stronger financial position to innovate and gain market share, while those with higher NPLs may be driven to innovate out of necessity to restore their competitiveness. This competitive pressure can accelerate the pace of innovation across the sector as banks strive to improve their offerings and operational efficiency [150]. Additionally, the interconnectedness of global financial markets means that high NPLs in one region can have spillover effects on other regions, prompting a global response that includes innovative financial solutions. For instance, cross-border lending and international banking practices may evolve in response to rising NPLs in emerging markets, leading to the development of more sophisticated risk assessment and management techniques on a global scale [151]. In conclusion, while high levels of NPLs indicate financial stress and potential economic downturns, they also create a fertile ground for innovation. The necessity to mitigate risks, adapt to new regulations, compete effectively, and respond to global financial dynamics drives banks and financial institutions to adopt innovative practices and technologies. This complex interplay ultimately contributes to the increase in the ISI despite the rise in NPLs, highlighting the resilience and adaptability of the financial sector in the face of challenges (see Figure 5).

The empirical findings also highlight that NPLs are negatively associated with the H-Index. This relationship is a complex issue rooted in the dynamics of economic instability, financial sector health, and the subsequent impact on academic productivity and citation impact. The H-Index measures both the productivity and citation impact of a researcher, institution, or journal, focusing on citable documents such as articles, reviews, and conference papers. When NPLs increase, it often reflects broader economic and financial stress that can adversely affect this index. Firstly, high levels of NPLs are indicative of economic instability and financial distress within the banking sector. This instability can lead to reduced funding for R&D activities as financial resources are redirected toward stabilizing the financial system and addressing the immediate crises caused by rising NPLs. For instance, during periods of economic downturns and increased NPLs, banks and other financial institutions may cut back on investments in academic and research institutions, leading to a decrease in the number of publications and a subsequent drop in the H-Index [144]. Moreover, economic policy uncertainty, which often accompanies rising NPLs, can further exacerbate the decline in academic productivity. Increased policy uncertainty can lead to a reduction in both public and private research funding as stakeholders adopt a more risk-averse approach. This reduction in funding translates to fewer research projects being undertaken, less innovative work being published, and consequently, a lower citation impact, as reflected in the H-Index [152]. Furthermore, the academic community is not immune to the broader economic conditions that affect the financial sector. Researchers, particularly those in economics and finance, may shift their focus from innovative research to more immediate, crisis-related analyses and publications. While this shift is critical for addressing the pressing issues of the time, it may not lead to high-impact, citable documents that contribute significantly to the H-Index. The urgency to address the financial crises can result in a proliferation of lower-impact publications that do not receive the same level of citations as more innovative, groundbreaking research [153]. Additionally, high levels of NPLs can influence the overall academic environment by creating a climate of uncertainty and financial constraint. This environment can discourage long-term research projects and collaborations that are often necessary for producing high-quality, citable research. Academic institutions may face budget cuts, leading to fewer opportunities for researchers to publish their work and engage in collaborative projects that enhance citation impact. The stress on financial systems due to high NPLs can also lead to a brain drain, where top researchers and academics seek more stable environments, further diminishing the potential for high-impact research output [150]. In conclusion, the increase in bank NPLs to total gross loans (%) negatively impacts citable documents’ H-Index by diverting financial resources away from R&D, increasing economic and policy uncertainty, and creating an academic environment less conducive to high-impact research. These factors collectively lead to a decrease in the number of high-quality, citable publications, thereby lowering the H-Index. Addressing the underlying causes of high NPLs and stabilizing the financial sector are crucial for maintaining robust academic productivity and citation impact (see Figure 6).

Moreover, NPLs decrease with the increase in ICTEXP due to several interconnected economic and financial factors that impede the overall efficiency and competitiveness of the ICT sector. Firstly, high levels of NPLs are indicative of financial instability and economic distress within a country. When banks accumulate a significant proportion of NPLs, it reflects poor financial health and heightened credit risk, which necessitates banks to divert their resources and focus on managing these non-performing assets rather than extending credit to other sectors, including ICT. This reallocation of resources can result in reduced availability of credit for businesses in the ICT sector, hampering their ability to invest in infrastructure, innovation, and expansion activities essential for competing in international markets. Secondly, economic uncertainty and instability, which are often associated with high NPL ratios, can deter both domestic and foreign investment in the ICT sector. Investors tend to be risk-averse during periods of financial instability, leading to a reduction in capital inflows necessary for developing ICT services. The decrease in investment can impede the growth and modernization of ICT infrastructure, thereby limiting the sector’s ability to offer competitive services in the global market [152]. Moreover, the banking sector’s health is closely linked to the overall economic environment, which in turn affects the ICT sector. A financially unstable banking system due to high NPLs can lead to higher borrowing costs and stricter lending criteria. These conditions can stifle entrepreneurial activities and innovation within the ICT sector as firms face higher hurdles in securing necessary funding for development and export activities. The increased cost of capital and reduced access to credit can significantly impede the growth of ICT services exports [153]. Additionally, high NPLs can lead to a broader economic slowdown, which impacts consumer demand for ICT services both domestically and internationally. As economic conditions deteriorate, businesses and consumers may cut back on spending, including on ICT services, which reduces the overall demand and, consequently, the export potential of these services. This demand reduction can have a cascading effect on the ICT sector, leading to decreased revenues and a lower contribution to total trade. Furthermore, the correlation between financial health and trade performance is also reflected in the competitiveness of the ICT sector on a global scale. Countries with stable financial systems and low NPL ratios tend to have better-developed ICT infrastructure and more robust export strategies. In contrast, countries struggling with high NPL ratios may lack the necessary financial stability to support their ICT services’ continuous development and competitiveness in the international market. The lack of a supportive financial environment can hinder the ability of ICT firms to innovate, scale, and compete globally [154]. In conclusion, the decrease in NPLs with the increase in ICTEXP can be attributed to the financial instability and economic uncertainty that high NPL ratios bring. These conditions reduce the availability of credit, deter investment, increase borrowing costs, and ultimately stifle the growth and competitiveness of the ICT sector in the global market. Addressing the underlying issues of high NPLs is crucial for fostering a stable financial environment conducive to ICT services’ growth and export potential (see Figure 7).

The negative relationship between ICTIMP and NPLs can be understood through a combination of economic strain, financial instability, and prioritization of domestic resources. This relationship highlights how the health of the banking sector can directly impact the import behaviors of an economy, particularly in sectors as vital as ICT services. ICT services imports encompass various critical components such as software, IT consulting, data processing, and telecommunication services. These imports are essential for maintaining and advancing technological infrastructure, supporting business operations, and fostering innovation. However, when the banking sector experiences a rise in NPL ratios, it signals broader economic and financial instability that can curtail the ability and willingness of a country to import such services. When the NPL ratio increases, it reflects a deteriorating financial situation within the banking sector, often due to borrowers’ inability to repay loans. This scenario typically arises during economic downturns or periods of financial distress. As banks struggle with higher NPLs, they become more risk-averse, leading to tighter credit conditions and reduced availability of capital for businesses and consumers. Consequently, companies, especially those in need of ICT services, may find it challenging to secure financing for imports, leading to a decrease in ICT services imports [151]. High NPL ratios can undermine confidence in the financial system, leading to broader economic instability. This instability can result in significant currency fluctuations, making imports more expensive. As the cost of importing ICT services rises, businesses may reduce their reliance on these imports to manage costs, further decreasing the percentage of ICT services in total trade. Financial instability often forces businesses to prioritize essential expenditures, sidelining investments in ICT services that might be deemed non-critical during economic hardships [123]. In response to rising NPL ratios and the associated financial strain, governments and businesses may shift their focus toward strengthening domestic capabilities. This shift can involve investing in local ICT service providers or developing in-house solutions to reduce dependency on costly imports. Such strategies are aimed at conserving foreign exchange and fostering local industry growth, which becomes more pronounced when the banking sector is under stress and external borrowing or investment is less viable [155]. High NPL ratios lead to stricter lending practices as banks seek to mitigate further risks. Companies facing credit constraints may delay or forgo investments in ICT services, which are often seen as capital-intensive. Instead, they might prioritize maintaining operational liquidity and addressing immediate financial obligations. This shift in investment priorities reduces the demand for ICT services imports, reflecting a broader trend of scaling back on expenditures perceived as less urgent [153]. Governments facing high levels of NPLs may implement protective trade policies to stabilize the economy. These policies could include increased tariffs on imported services, including ICT, to protect domestic industries and conserve foreign currency reserves. Such measures directly reduce the import levels of ICT services, as higher costs deter businesses from procuring services from abroad [156]. These factors collectively discourage the import of ICT services, reflecting a strategic adjustment by both businesses and governments to navigate financial challenges (see Figure 8).

Finally, the level of NPLs decrease with the increase in ICT due to several interrelated factors that undermine economic stability, financial resource allocation, and overall confidence in the banking sector. High levels of NPLs indicate financial distress within banks, which has a ripple effect on the broader economy, including ICT infrastructure and investments. This relationship can be dissected into a few key areas. Firstly, high NPLs signify poor financial health of banks, which in turn constrains their ability to extend credit to various sectors, including ICT. When banks face a high proportion of bad loans, they need to allocate more resources to cover potential losses, leading to tightened lending standards and reduced availability of credit. This credit crunch affects businesses across the economy, including those in the ICT sector, which rely heavily on continuous investments for infrastructure development, technological advancements, and innovation [144]. Secondly, economic instability, which often accompanies rising NPLs, can deter both domestic and foreign investment. Investors seek stable environments where the risk of financial loss is minimized. High NPLs signal financial instability and increased risk, causing investors to become cautious or withdraw their investments altogether. This reduction in investment particularly affects capital-intensive sectors like ICT, which require substantial and ongoing financial inputs to maintain and upgrade infrastructure, adopt new technologies, and support innovation activities [152]. Additionally, financial instability reflected by high NPLs can lead to an overall economic downturn. In such periods, both businesses and consumers may cut back on spending, including ICT services and products. The decreased demand can limit the revenue streams for ICT companies, making it challenging for them to sustain operations, invest in new technologies, or expand their services. This slowdown directly impacts the ICT sector’s contribution to the GII [153]. Moreover, the banking sector’s focus shifts significantly when dealing with high levels of NPLs. Banks must manage these bad loans, which often involve restructuring, recovering bad debts, and dealing with legal issues. This diversion of focus and resources means that banks have less capacity to support ICT projects through loans or other financial services. This lack of support can stifle the growth and development of the ICT sector, as it depends heavily on bank financing for both operational and capital expenditures [150]. Furthermore, the presence of high NPLs can also undermine the trust and confidence in the financial system. A banking sector plagued with bad loans can erode confidence among businesses and consumers alike, leading to decreased economic activity. When trust in the financial system wanes, it can result in a lower rate of adoption and integration of ICT solutions as both individuals and businesses might be reluctant to invest in new technologies or digital transformations that require high upfront costs and rely on a stable financial environment [154]. In conclusion, the negative relationship between ICT and NPLs can be attributed to the financial strain on banks, reduced investments, economic instability, decreased demand, and a shift in banking priorities. These factors collectively hinder the growth and development of ICT infrastructure and services, ultimately affecting the country’s overall innovation capacity (see Figure 9).

7. Policy Implications

Following the empirical results, countries with high NPL ratios should consider adopting international banking standards such as Basel III, which emphasizes capital adequacy, stress testing, and market liquidity risk management. These standards can help stabilize financial institutions by ensuring they maintain sufficient capital buffers and manage risks more effectively. For example, Greece and Cyprus, which have faced significant financial crises, could benefit from stricter adherence to Basel III standards to prevent future banking sector distress. In addition to adopting international standards, enhancing regulatory frameworks is crucial. Strengthening regulatory oversight ensures that banks adhere to prudent lending practices and maintain healthy loan portfolios. Regulatory bodies should be empowered to enforce stringent loan classification and provisioning guidelines. For instance, implementing robust regulatory frameworks in the Central African Republic and Chad could improve banking practices and reduce NPL ratios [157,158].

Reducing sectoral dependence is essential for countries with high NPLs, which often rely heavily on a few sectors, making them vulnerable to economic shocks. Promoting diversification can mitigate this risk by encouraging investment in various sectors, including manufacturing, services, and technology. Countries like Equatorial Guinea and Chad, which depend heavily on oil, should invest in other industries to stabilize their economies. Supporting SMEs and entrepreneurship can drive economic diversification and innovation. Policies that provide financial support, tax incentives, and technical assistance to SMEs can foster a more resilient economy. For example, initiatives like microfinance programs in Bangladesh have successfully supported SMEs, contributing to economic growth and stability [159,160]. Promoting good governance is critical for economic growth and financial stability. Efforts to reduce corruption, enhance transparency, and strengthen legal frameworks are essential. For instance, Ukraine and Chad should focus on improving governance and reducing political instability to create a conducive environment for economic activities. Strengthening institutions that uphold property rights, enforce contracts, and provide reliable legal recourse is foundational to a healthy banking sector. Improving institutional quality in countries like the Central African Republic can help build trust in the financial system and reduce NPLs [161,162].

Investing in ICT infrastructure is vital for innovation. Governments should invest in ICT infrastructure to support digital transformation and enhance productivity. Countries like Kenya and India have made significant investments in ICT, boosting innovation and economic growth. Increased funding for R&D can drive innovation and economic diversification. Public and private sector collaboration in R&D should be encouraged. For example, China’s significant investment in R&D has positioned it as a global leader in innovation [163]. Financial inclusion initiatives can ensure that individuals and businesses have access to necessary financial services, reducing the risk of loan defaults. Policies should promote the availability of affordable credit, savings, and insurance products. India’s financial inclusion programs, such as the Pradhan Mantri Jan Dhan Yojana, have increased millions of people’s access to banking services. Enhancing financial literacy programs can educate borrowers on prudent financial management, reducing the likelihood of loan defaults. Financial education campaigns in countries like Brazil have improved financial literacy, contributing to better loan repayment rates [164].

Establishing Asset Management Companies (AMCs) can help banks offload NPLs, clean their balance sheets, and focus on new lending. Malaysia’s Danaharta and Korea’s KAMCO are successful examples of AMCs that managed NPLs effectively post-crisis. Loan restructuring programs can provide relief to distressed borrowers while helping banks recover some value from NPLs. Italy’s GACS scheme, which guarantees securitized NPLs, has been effective in reducing NPL ratios. International financial institutions like the IMF and World Bank can provide financial assistance and technical support to countries struggling with high NPLs. Greece received substantial support from the IMF and EU during its financial crisis, which helped stabilize its banking sector. Countries can benefit from sharing knowledge and best practices on NPL management and innovation promotion. Regular forums and workshops organized by international bodies can facilitate this exchange of ideas and strategies [165,166]. Promoting green financing can drive investments in sustainable projects, reducing economic volatility and fostering long-term growth. The European Union’s Green Deal includes financial mechanisms to support sustainable development, which can also help stabilize the financial sector. Encouraging corporate social responsibility (CSR) among banks can lead to more responsible lending practices, reducing the risk of NPLs. Banks that integrate CSR into their operations tend to focus on long-term sustainability, improving overall financial stability. Continuous monitoring of banks’ financial health through regular assessments and stress tests can pre-emptively identify risks. The European Central Bank conducts regular stress tests on European banks to ensure financial stability. Policymakers should utilize data and analytics to inform decisions on NPL management and innovation promotion. Countries like Singapore use advanced data analytics to monitor financial sector health and guide policy interventions [167,168].

The analysis of NPLs and their impact on innovation systems provides valuable insights into the health of banking sectors worldwide. Addressing high NPL ratios requires comprehensive policy interventions focused on strengthening financial regulation, promoting economic diversification, ensuring political stability, enhancing innovation, improving financial inclusion, and fostering international cooperation. By implementing these policy recommendations, countries can manage NPLs effectively and create robust innovation systems that drive sustainable economic growth.

8. Discussion

The empirical evidence of this research has brought out complex interrelations between financial instability, measured by NPLs, and many innovation indicators. One of the main results possibly derived is the positive relationship of NPLs with exports for the CCS sectors; this breaks the conceptual viewpoint on the damage uniformly caused by financial instability to all innovation sectors. Rather, this indicates that some sectors—particularly those in creative industries—are resilient and could have the potential to perform well under financial distress. This positive relationship between NPLs and CCS exports can be based on the very nature of CCS. These sectors often source alternative means of financing, from public funds to grants, while they probably are not as dependent on bank finance as, for example, sectors in information and communications technologies. What is more, during economic instability, there is quite an even demand for cultural products and services or an increase because consumers seek less expensive modes of spending on entertainment and information consumption. This partly explains the paradoxical trend: export growth continues to rise sharply—in tandem with rising NPLs—because of the demand and opportunities the sector offers, along with its rapid adaptability and innovativeness. In stark contrast to the CCS sectors, the ICT sector shows a negative relationship with NPLs. This finding highlights the vulnerability of ICT services exports and imports to financial instability. As ICT industries typically require significant investment in infrastructure, research, and technological development, they are more dependent on access to stable credit flows. In periods of heightened NPLs, banks are more likely to tighten their lending standards, which directly impacts the ability of ICT firms to secure funding for innovation and expansion. The negative relationship between NPLs and ICT indicators suggests that technological progress is more sensitive to financial conditions than creative sectors. This may be due to the capital-intensive nature of ICT development, which requires long-term investments that become increasingly difficult to secure when banks are managing high levels of non-performing loans. The findings suggest that in times of financial distress, governments and financial institutions may need to prioritize support for the ICT sector to maintain technological advancement and global competitiveness [169,170,171].

The negative correlation of NPLs with the H-Index, an indicator of academic and research output, only points out the wider implications that financial instability has on intellectual innovation. A larger NPL ratio would indicate strained financial conditions of a country and thus lesser funding toward any R&D. This, in turn, itself impacts the high-quality citable work of academics and researchers with a lower H-Index. This can reduce the resources that are available to carry out innovative projects, both independently and in relation to other institutions, thus limiting the flow of funds into research institutions. That is quite essential, especially in a situation where public universities and research centers are funded by government budgets. This further contracts the available funds, reducing opportunities for research, collaboration, and publication, whose end is a reduced global impact and visibility of the country’s academic contribution. The positive link between ISI and NPLs is one of the most nuanced findings of this study. This may look strange at first, for dismally high levels of NPLs are indicative of a financially weakened financial system. Again, if analyzed closely, such times of financial distress urge firms and the financial world to look for survival through innovative measures. This has further motivated banks and companies to rethink their strategies and invest in new technologies while developing efficient systems of risk management in the face of increasing NPLs. Regulatory bodies could be on their toes to come up with new measures and frameworks that would encourage financial stability through innovation. It is the need to cope with very difficult economic circumstances that can provide the strongest stimulus to the development of major elements of the financial and regulatory infrastructure, and for which increased ISI might provide a reasonable accommodation of that consideration [172,173].

The implications of this analysis overall suggest a number of important policy conclusions, particularly for countries under the highest level of NPLs. For the nations in which the financial distress is biased toward the ICT sector, state interventions may be required for the support of technical innovation. This might be best achieved through government intervention to provide these ICT firms with subsidies, alternative financing mechanisms, or tax incentives to sustain competitiveness and innovation during such economic distress. On the other hand, the case of growing CCS industries in some countries can support an indication of the possibility that the CCS industries will consistently thrive despite the economic turmoil. Policymakers, therefore, can build economic resilience by adding more support to creative industries; this gives CCS resilience that countries could exploit by consciously investing in and capturing the burgeoning creative and cultural exports that seem less constrained by high NPLs. To this end, one may say that the relationship between NPLs and financial stability, on the one hand, and innovation, on the other, is quite complex. From this study, the findings suggest that while this financial distress may constrain the growth of innovative industries relating to capital intensity, such as ICT, other sectors that are more flexible and stronger, such as the CCS, may still be able to emerge. These results would stand, therefore, as a very firm base for further research and an important guide for policymakers in developing policies that can stimulate innovation during periods of financial stringency [174,175].

The major implications of this article are centered on the relationship between non-performing loans and innovation, particularly how financial distress, as represented by high levels of non-performing loans, influences sectoral innovation differentially. Among the findings, the high NPL ratio, which usually signals financial instability, impacts differential industries such as ICT and CCS in a distinctive manner. For instance, financial instability tends to dampen innovative investment in capital-intensive sectors like ICT due to reduced credit availability. In contrast, the creative industries, as proxied by CCS, are resistant to these shocks and can, therefore, flourish under such conditions—captured in the strongly positive relationship between NPLs and CCS exports. These findings also carry some political consequences and suggest sector-specific policy interventions. Policymakers are also urged to provide alternative financing means, such as government-backed loans or fiscal incentives, that support innovation in ICT, an industry where innovation is key to global competitiveness in the digital economy. On the other hand, creative industries need less intervention but may profit from supportive policies in terms of lower taxes or tailored financial instruments. Another important implication concerns academic productivity and research. This paper shows that financial constraint, proxied by high NPLs, suppresses research output, especially as measured by the H-Index. Such a finding points to the necessity of ensuring that, in times of financial turmoil, funding to academic and research institutions is maintained or increased lest long-term intellectual stagnation ensues. In addition, this article postulates that financial crises may also be a catalyst for innovation within the financial sector itself. On the macroeconomic level, the negative relationship between NPLs and innovation underlines the need for balanced regulatory frameworks that protect financial stability while fostering innovation. Economies with lower ratios of NPLs are in a position to invest more in new technologies that support innovative activities. A high degree of NPLs constrains this, increasing the gap between developed and developing economies. What this further suggests politically is that governments in such countries with high NPL ratios must give greater political priority to reforms in the financial sector and embrace measures that stimulate innovation in all industries. International cooperation, especially on financial regulation, would therefore become imperative in order to ensure stability in the global markets and create an environment that fosters innovation.

9. Conclusions

This research tried to isolate the role of NPLs in the nexus between financial stability and innovation technology. The empirical analysis used clustering methods (k-means) together with panel data regressions. The findings highlight that NPLs play a key role in the delicate balance between financial stability and innovation. These loans, which are in default or close to default, are a critical indicator of financial distress within banking institutions and the broader economy. High levels of NPLs signify underlying economic problems, such as poor credit risk management and economic downturns, which can lead to significant financial instability. However, these challenging circumstances also drive the necessity for financial institutions to innovate in their risk management and lending practices. Financial institutions, faced with the pressure of managing high NPL levels, are compelled to adopt advanced technologies and develop innovative strategies to mitigate these risks. Effective management of NPLs is crucial for maintaining financial stability. Mechanisms such as asset quality reviews, which involve comprehensive assessments of the value and performance of a bank’s assets, are vital for identifying and addressing problematic loans. Additionally, the establishment of AMCs can facilitate the resolution of NPLs by buying and restructuring these distressed assets, thus cleaning up banks’ balance sheets. Robust legal frameworks for insolvency and debt recovery are also essential, providing clear guidelines and processes for managing defaults and recovering assets. These measures not only help in cleaning up banks’ balance sheets but also restore confidence in the financial system. Restoring confidence is critical for fostering an environment conducive to innovation as it encourages investment and the development of new financial products and services.

Moreover, financial innovation can contribute significantly to reducing NPLs by improving credit assessment processes. Innovations in data analytics and artificial intelligence can enhance the accuracy of credit scoring models, allowing banks to better assess the creditworthiness of borrowers and reduce the likelihood of defaults. Enhanced monitoring systems, utilizing technologies such as blockchain and real-time data analysis, enable more effective tracking of loan performance and early identification of potential issues. Additionally, providing flexible financing options, such as variable repayment schedules and tailored loan products, can help borrowers manage their debt more effectively and reduce the risk of default. However, the rapid growth of financial technologies and the fintech sector presents new challenges that require adaptive regulatory frameworks to manage associated risks. Fintech companies, with their innovative approaches to financial services, have the potential to disrupt traditional banking models and introduce new risks into the financial system. Regulators must develop strategies to oversee these new entities, ensuring that they adhere to sound risk management practices and do not contribute to financial instability. This includes updating existing regulations to address the unique risks posed by digital financial services and ensuring that fintech companies are subject to the same scrutiny as traditional banks. This interplay between NPLs, financial stability, and innovation again highlights the need for a balanced regulatory approach that encourages technological advancements while mitigating potential systemic risks. Policymakers must strike a delicate balance between promoting innovation and maintaining a stable financial environment. This involves supporting the development and adoption of new technologies and ensuring that these innovations do not introduce new vulnerabilities into the financial system. Effective regulation should be forward-looking, anticipating future challenges and adapting to the evolving financial landscape. Ultimately, by addressing the challenges associated with NPLs and leveraging the potential of financial innovation, policymakers can ensure a stable and resilient financial system that supports sustainable economic growth and development. This requires a coordinated effort between regulators, financial institutions, and fintech companies to create an environment where innovation can thrive without compromising financial stability. By fostering a culture of innovation and prudence, the financial system can become more robust and better equipped to support long-term economic development.

The specific contribution of this article consists of the empirical results that illuminate the relationship between financial instability and innovation systems at the country level. First of all, three clusters have been identified using the unsupervised and machine-learning algorithm k-means optimized with the Elbow Method. Furthermore, the panel data analysis has shown that the level of NPLs is positively associated with cultural and creative services exports as a percentage of the total trade and innovation input sub-index and negatively associated with the Hirsch Index, ICT services exports as a percentage of total trade, and ICT services imports as a percentage of total trade, and information and communication technologies.

Policymakers should henceforth address the different impacts that NPLs have on innovation across sectors. Financial instability normally affects industries differently, and while creative ones like cultural and creative services have proven more resilient, those that are highly capital-intensive, like ICT, have proven particularly vulnerable. The government should give them special support in bad economic times through alternative financing mechanisms that keep innovations running, like government-backed loans or subsidies. Furthermore, regulatory frameworks should be further improved to reach a situation whereby financial institutions can foster innovative risk management approaches, such as AI use in credit scoring, without compromising the stability of the financial systems. Further, the spiral of rising NPLs will be muted through a measured approach impregnated with systems like Basel III. Finally, promoting financial innovation—including sophisticated technologies like blockchain—will improve credit risk management and help reduce NPLs for times to come. This focus on fostering stability and promoting innovation will result in a resilient and competitive economy in the long run.

A limitation of this study is related to external factors that are not considered in the analysis. Global economic states, geopolitical shocks, and shifts in public policy have a greater bearing on financial stability and innovation. For instance, even the COVID-19 pandemic drastically changed financial and innovation landscapes worldwide.

Future research based on the findings of this study can be suggested for a number of critical domains: first, other studies may probe into the implications of financial instability on other high-tech industries outside the ICT sector but within similar contingencies of credit flows. It also points out that research is needed into how NPLs affect specific metrics on innovation in different economies since this study focused on a general set of innovation indices in the economy. Further, some cross-country comparisons indicate meaningful implications of the different ways that financial policies and institutional frameworks moderate the relationship between financial instability and innovation. Finally, in light of these findings concerning the resilience of the creative industry during financial crises, future research should investigate the mechanisms by which cultural and creative industries may serve as stabilizers in a distressed economy and whether targeted interventions in this industry sector might foster longer economic recovery.